Assessment of two aerosol optical thickness retrieval algorithms applied to MODIS aqua and terra measurements in Europe

© Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 LicenseThe aim of the present study is to validate AOT (aerosol optical thickness) and A° ngström exponent (α), obtained from MODIS (MODerate resolution Imaging Spectroradiometer) Aqua and Terra calibrated level 1 data (1 km horizontal resolution at ground) with the SAER (Satellite AErosol Retrieval) algorithm and with MODIS Collection 5 (c005) standard product retrievals (10 km horizontal resolution), against AERONET (AErosol RObotic NETwork) sun photometer observations over land surfaces in Europe. An inter-comparison of AOT at 0.469 nm obtained with the two algorithms has also been performed. The time periods investigated were chosen to enable a validation of the findings of the two algorithms for a maximal possible variation in sun elevation. The satellite retrievals were also performed with a significant variation in the satellite-viewing geometry, since Aqua and Terra passed the investigation area twice a day for several of the cases analyzed. The validation with AERONET shows that the AOT at 0.469 and 0.555 nm obtained with MODIS c005 is within the expected uncertainty of one standard deviation of the MODIS c005 retrievals (1AOT =±0.05±0.15 ·AOT). The AOT at 0.443 nm retrieved with SAER, but with a much finer spatial resolution, also agreed reasonably well with AERONET measurements. The majority of the SAER AOT values are within the MODIS c005 expected uncertainty range, although somewhat larger average absolute deviation occurs compared to the results obtained with the MODIS c005 algorithm. The discrepancy between AOT from SAER and AERONET is, however, substantially larger for the wavelength 488 nm. This means that the values are, to a larger extent, outside of the expected MODIS uncertainty range. In addition, both satellite retrieval algorithms are unable to estimate accurately, although the MODIS c005 algorithm performs better. Based on the inter-comparison of the SAER and MODIS c005 algorithms, it was found that SAER on the whole is able to obtain results within the expected uncertainty range of MODIS Aqua and Terra observations.Peer reviewe

Aerosol optical depth retrieval from the EarthCARE Multi-Spectral Imager: the M-AOT product

The Earth Explorer mission Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) will not only provide profile information on aerosols but also deliver a horizontal context to it through measurements by its Multi-Spectral Imager (MSI). The columnar aerosol product relying on these passive signals is called M-AOT (MSI-Aerosol Optical Thickness). Its main parameters are aerosol optical thickness (AOT) at 670 nm over ocean and valid land pixels and at 865 nm over ocean. Here, the algorithm and assumptions behind it are presented. Further, first examples of product parameters are given based on applying the algorithm to simulated EarthCARE test data and Moderate Resolution Imaging Spectroradiometer (MODIS) Level-1 data. Comparisons to input fields used for simulations, to the official MODIS aerosol product, to AErosol RObotic NETwork (AERONET) and to Maritime Aerosol Network (MAN) show an overall reasonable agreement. Over ocean, correlations are 0.98 (simulated scenes), 0.96 (compared to MYD04) and 0.9 (compared to MAN). Over land, correlations are 0.62 (simulated scenes), 0.87 (compared to MYD04) and 0.77 (compared to AERONET). A concluding discussion will focus on future improvements that are necessary and envisioned to enhance the product

A new satellite-based global climatology of dust aerosol optical depth

By mass, dust is the largest contributor to global aerosol burden, yet long-term observational records of dust, particularly over the ocean, are limited. Here, two nearly global observational datasets of dust aerosol optical depth τd are created primarily on the basis of optical measurements of the aerosol column from 1) the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite spanning from 2001 to 2018 and 2) the Advanced Very High Resolution Radiometer (AVHRR) from 1981 to 2018. The quality of the new data is assessed by comparison with existing dust datasets that are spatially more limited. Between 2001 and 2018, τd decreased over Asia and increased significantly over the Sahara, Middle East, and parts of eastern Europe, with the largest increase found over the Aral Sea where emissive playa surfaces have been exposed. These daily, observational, and nearly global records of dust will allow for improvement in understanding the role of dust in climate variability

Trends in MODIS and AERONET derived aerosol optical thickness over Northern Europe

Long-term Aqua and Terra MODIS (MODerate resolution Imaging Spectroradiometer) Collections 5.1 and 6.1 (c051 and c061, respectively) aerosol data have been combined with AERONET (AERosol RObotic NETwork) ground-based sun photometer observations to examine trends in aerosol optical thickness (AOT, at 550 nm) over Northern Europe for the months April to September. For the 1927 and 1559 daily coincident measurements that were obtained for c051 and c061, respectively, MODIS AOT varied by 86 and 90%, respectively, within the predicted uncertainty of one standard deviation of the retrieval over land (ΔAOT = ±0.05 ± 0.15·AOT). For the coastal AERONET site Gustav Dalen Tower (GDT), Sweden, larger deviations were found for MODIS c051 and c061 (79% and 75%, respectively, within predicted uncertainty). The Baltic Sea provides substantially better statistical representation of AOT than the surrounding land areas and therefore favours the investigations of trends in AOT over the region. Negative trends of 1.5% and 1.2% per year in AOT, based on daily averaging, were found for the southwestern Baltic Sea from MODIS c051 and c061, respectively. This is in line with a decrease of 1.2% per year in AOT at the AERONET station Hamburg. For the western Gotland Basin area, Sweden, negative trends of 1.5%, 1.1% and 1.6% per year in AOT have been found for MODIS c051, MODIS c061 and AERONET GDT, respectively. The strongest trend of –1.8% per year in AOT was found for AERONET Belsk, Poland, which can be compared to –1.5% per day obtained from MODIS c051 over central Poland. The trends in MODIS and AERONET AOT are nearly all statistically significant at the 95% confidence level. The strongest aerosol sources are suggested to be located southwest, south and southeast of the investigation area, although the highest prevalence of pollution events is associated with air mass transport from southwest.Peer reviewe

Improved Cloud and Snow Screening in MAIAC Aerosol Retrievals Using Spectral and Spatial Analysis

An improved cloud/snow screening technique in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is described. It is implemented as part of MAIAC aerosol retrievals based on analysis of spectral residuals and spatial variability. Comparisons with AERONET aerosol observations and a large-scale MODIS data analysis show strong suppression of aerosol optical thickness outliers due to unresolved clouds and snow. At the same time, the developed filter does not reduce the aerosol retrieval capability at high 1 km resolution in strongly inhomogeneous environments, such as near centers of the active fires. Despite significant improvement, the optical depth outliers in high spatial resolution data are and will remain the problem to be addressed by the application-dependent specialized filtering techniques

Evaluation of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Algorithm through Intercomparison with VIIRS Aerosol Products and AERONET

The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is under evaluation for use in conjunction with the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission. Column aerosol optical thickness (AOT) data from MAIAC are compared against corresponding data. from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument over North America during 2013. Product coverage and retrieval strategy, along with regional variations in AOT through comparison of both matched and un-matched seasonally gridded data are reviewed. MAIAC shows extended coverage over parts of the continent when compared to VIIRS, owing to its pixel selection process and ability to retrieve aerosol information over brighter surfaces. To estimate data accuracy, both products are compared with AERONET Level 2 measurements to determine the amount of error present and discover if there is any dependency on viewing geometry and/or surface characteristics. Results suggest that MAIAC performs well over this region with a relatively small bias of -0.01; however there is a tendency for greater negative biases over bright surfaces and at larger scattering angles. Additional analysis over an expanded area and longer time period are likely needed to determine a comprehensive assessment of the products capability over the Western Hemisphere. and meet the levels of accuracy needed for aerosol monitoring

Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI

Mitigating the impact of atmospheric effects on optical remote sensing data is critical for monitoring intrinsic land processes and developing Analysis Ready Data (ARD). This work develops an approach to this for the NERC NCEO medium resolution ARD Landsat 8 (L8) and Sentinel 2 (S2) products, called Sensor Invariant Atmospheric Correction (SIAC). The contribution of the work is to phrase and solve that problem within a probabilistic (Bayesian) framework for medium resolution multispectral sensors S2/MSI and L8/OLI and to provide per-pixel uncertainty estimates traceable from assumed top-of-atmosphere (TOA) measurement uncertainty, making progress towards an important aspect of CEOS ARD target requirements. A set of observational and a priori constraints are developed in SIAC to constrain an estimate of coarse resolution (500 m) aerosol optical thickness (AOT) and total column water vapour (TCWV), along with associated uncertainty. This is then used to estimate the medium resolution (10–60 m) surface reflectance and uncertainty, given an assumed uncertainty of 5 % in TOA reflectance. The coarse resolution a priori constraints used are the MODIS MCD43 BRDF/Albedo product, giving a constraint on 500 m surface reflectance, and the Copernicus Atmosphere Monitoring Service (CAMS) operational forecasts of AOT and TCWV, providing estimates of atmospheric state at core 40 km spatial resolution, with an associated 500 m resolution spatial correlation model. The mapping in spatial scale between medium resolution observations and the coarser resolution constraints is achieved using a calibrated effective point spread function for MCD43. Efficient approximations (emulators) to the outputs of the 6S atmospheric radiative transfer code are used to estimate the state parameters in the atmospheric correction stage. SIAC is demonstrated for a set of global S2 and L8 images covering AERONET and RadCalNet sites. AOT retrievals show a very high correlation to AERONET estimates (correlation coefficient around 0.86, RMSE of 0.07 for both sensors), although with a small bias in AOT. TCWV is accurately retrieved from both sensors (correlation coefficient over 0.96, RMSE <0.32 g cm−2). Comparisons with in situ surface reflectance measurements from the RadCalNet network show that SIAC provides accurate estimates of surface reflectance across the entire spectrum, with RMSE mismatches with the reference data between 0.01 and 0.02 in units of reflectance for both S2 and L8. For near-simultaneous S2 and L8 acquisitions, there is a very tight relationship (correlation coefficient over 0.95 for all common bands) between surface reflectance from both sensors, with negligible biases. Uncertainty estimates are assessed through discrepancy analysis and are found to provide viable estimates for AOT and TCWV. For surface reflectance, they give conservative estimates of uncertainty, suggesting that a lower estimate of TOA reflectance uncertainty might be appropriate

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas

The Time Series Technique for Aerosol Retrievals over Land from MODIS: Algorithm MAIAC

Atmospheric aerosols interact with sun light by scattering and absorbing radiation. By changing irradiance of the Earth surface, modifying cloud fractional cover and microphysical properties and a number of other mechanisms, they affect the energy balance, hydrological cycle, and planetary climate [IPCC, 2007]. In many world regions there is a growing impact of aerosols on air quality and human health. The Earth Observing System [NASA, 1999] initiated high quality global Earth observations and operational aerosol retrievals over land. With the wide swath (2300 km) of MODIS instrument, the MODIS Dark Target algorithm [Kaufman et al., 1997; Remer et al., 2005; Levy et al., 2007] currently complemented with the Deep Blue method [Hsu et al., 2004] provides daily global view of planetary atmospheric aerosol. The MISR algorithm [Martonchik et al., 1998; Diner et al., 2005] makes high quality aerosol retrievals in 300 km swaths covering the globe in 8 days. With MODIS aerosol program being very successful, there are still several unresolved issues in the retrieval algorithms. The current processing is pixel-based and relies on a single-orbit data. Such an approach produces a single measurement for every pixel characterized by two main unknowns, aerosol optical thickness (AOT) and surface reflectance (SR). This lack of information constitutes a fundamental problem of the remote sensing which cannot be resolved without a priori information. For example, MODIS Dark Target algorithm makes spectral assumptions about surface reflectance, whereas the Deep Blue method uses ancillary global database of surface reflectance composed from minimal monthly measurements with Rayleigh correction. Both algorithms use Lambertian surface model. The surface-related assumptions in the aerosol retrievals may affect subsequent atmospheric correction in unintended way. For example, the Dark Target algorithm uses an empirical relationship to predict SR in the Blue (B3) and Red (B1) bands from the 2.1 m channel (B7) for the purpose of aerosol retrieval. Obviously, the subsequent atmospheric correction will produce the same SR in the red and blue bands as predicted, i.e. an empirical function of 2.1. In other words, the spectral, spatial and temporal variability of surface reflectance in the Blue and Red bands appears borrowed from band B7. This may have certain implications for the vegetation and global carbon analysis because the chlorophyll-sensing bands B1, B3 are effectively substituted in terms of variability by band B7, which is sensitive to the plant liquid water. This chapter describes a new recently developed generic aerosol-surface retrieval algorithm for MODIS. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm simultaneously retrieves AOT and surface bi-directional reflection factor (BRF) using the time series of MODIS measurements

Seven year particulate matter air quality assessment from surface and satellite measurements

International audienceUsing seven years of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness (AOT) data and ground measurements of particulate matter mass over one site in the Southeastern United States (33.55 N, 86.82 W) we present a comprehensive analysis of various aspects of particulate matter air quality. Monthly, seasonal and inter-annual relationships are examined with emphasis on sampling biases, quality indicators in the AOT product and various cloud clearing criteria. Our results indicate that PM2.5 mass concentration over Northern Birmingham has decreased by about 23% in year 2006 when compared to year 2002 and air quality during summer months are poor when compared to winter months. MODIS-Terra AOT data was available only about 50% of the time due to cloud cover and favorable surface conditions. However, the mean difference in monthly mean PM2.5 was less than 2.2 ?gm?3 derived using all the data and from only those days when satellite AOT was available indicating that satellite data does not have sampling issues. The correlation between PM2.5 and MODIS AOT increased from 0.52 to 0.62 when hourly PM2.5 data were used instead of daily mean PM2.5 data. Changing box size for satellite data around the ground station during comparisons produced less than ±0.03 difference in mean AOT values for 90% of observations. Application of AOT quality flags reduced the sample size but does not affect AOT-PM2.5 relationship significantly. We recommend using AOT quality flags for daily analysis, whereas long time scale analysis can be performed without using all AOT retrievals to obtain better sampling. Our analysis indicates that satellite data is a useful tool for monitoring particulate matter air quality especially in regions where ground measurements are not available